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This knowledge area embodies a variety of data driven analytics, geocomputational methods, simulation and model driven approaches designed to study complex spatial-temporal problems, develop insights into characteristics of geospatial data sets, create and test geospatial process models, and construct knowledge of the behavior of geographically-explicit and dynamic processes and their patterns.

Topics in this Knowledge Area are listed thematically below. Existing topics are in regular font and linked directly to their original entries (published in 2006; these contain only Learning Objectives). Entries that have been updated and expanded are in bold. Forthcoming, future topics are italicized.

Describe a “bottom-up” simulation from an activity-perspective with changes in the locations and/or activities the individual person (and/or vehicle) in space and time, in the activity patterns and space-time trajectories created by these activity patterns, and in the consequent emergent phenomena, such as traffic jams and land-use patterns

Describe how various parameters in an agent-based model can be modified to evaluate the range of behaviors possible with a model specification

Describe how measurements on the output of a model can be used to describe model behavior

Modelling accessibility involves combining ideas about destinations, distance, time, and impedances to measure the relative difficulty an individual or aggregate region faces when attempting to reach a facility, service, or resource. In its simplest form, modelling accessibility is about quantifying movement opportunity. Crucial to modelling accessibility is the calculation of the distance, time, or cost distance between two (or more) locations, which is an operation that geographic information systems (GIS) have been designed to accomplish. Measures and models of accessibility thus draw heavily on the algorithms embedded in a GIS and represent one of the key applied areas of GIS&T.

List the conditions that make point pattern analysis a suitable process

Identify the various ways point patterns may be described

Identify various types of K-function analysis

Describe how Independent Random Process/Chi-Squared Result (IRP/CSR) may be used to make statistical statements about point patterns

Outline measures of pattern based on first and second order properties such as the mean center and standard distance, quadrat counts, nearest neighbor distance, and the more modern G, F, and K functions

Outline the basis of classic critiques of spatial statistical analysis in the context of point pattern analysis

Explain how distance-based methods of point pattern measurement can be derived from a distance matrix

Explain how proximity polygons (e.g., Thiessen polygons) may be used to describe point patterns

Explain how the K function provides a scale-dependent measure of dispersion

Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics